Dynamic determination of video stream quality discrepancies in a communication session

1 . A method, comprising:

receiving, at a server, an outgoing video stream from a transmitting device to a receiving device;

identifying a first set of video parameter data corresponding to a quality of the outgoing video stream, wherein identifying the first set of video parameter data comprises:

training a machine learning (ML) model to determine a set of video parameters related to video stream optimization;

inputting the outgoing video stream into the trained ML model; and

identifying, via the trained ML model, video parameter data from the outgoing video stream correlating to each of the determined video parameters;

receiving, from the receiving device, a second set of video parameter data corresponding to the quality of the video stream;

determining one or more quality discrepancies between the first set of video parameter data and the second set of video parameter data; and

providing notification of at least a subset of the quality discrepancies to one or both of the transmitting device and the receiving device.

2 . The method of claim 1 , wherein determining the one or more quality discrepancies between the first set of video parameter data and the second set of video parameter data is performed using one or more ML techniques.

3 . The method of claim 1 , wherein the received second set of video parameter data comprises a same video parameter types as the first set of video parameter data.

4 . The method of claim 1 , wherein the determined one or more quality discrepancies comprise a discrepancy in one or more of: video resolution, and video frame rate.

5 . The method of claim 1 , wherein the transmitting device and receiving device are configured to transmit data between one another directly via a peer-to-peer network.

6 . The method of claim 1 , wherein the determined one or more quality discrepancies comprise a discrepancy in replacement rate of pixels shared between the outgoing video stream and an incoming video stream.

7 . The method of claim 1 , wherein the outgoing video stream further comprises an audio signal, and wherein the determined one or more quality discrepancies comprise a discrepancy in audio quality.

8 . The method of claim 7 , wherein the discrepancy in audio quality comprises a discrepancy in audio bit rate.

9 . The method of claim 1 , further comprising:

determining, based on the one or more quality discrepancies, one or more optimization actions to be performed on an incoming video stream;

generating a processed video stream by performing the one or more optimization actions on the incoming video stream; and

sending the processed video stream to the receiving device as a substitute for the incoming video stream.

10 . The method of claim 9 , wherein the one or more optimization actions comprise reducing a color space of at least a portion of the incoming video stream.

11 . The method of claim 9 , wherein the one or more optimization actions comprise:

segmenting, based on the one or more quality discrepancies, the incoming video stream into a plurality of regions, and

applying one or both of: a quality reduction to one or more video parameters within a subset of the regions, and a quality increase to one or more video parameters in a subset of the regions.

12 . The method of claim 11 , wherein a quality reduction to one or more video parameters is applied, the quality reduction comprising a selective refresh of frames from the incoming video stream within a subset of the regions.

13 . The method of claim 11 , wherein shared content is presented within a region of the incoming video stream, and wherein the segmenting of the incoming video stream is performed based on the region where the shared content is presented.

14 . The method of claim 9 , wherein the outgoing video stream further comprises an audio signal, and wherein the one or more optimization actions comprise converting the audio signal of the incoming video stream from a stereophonic audio signal to a monophonic audio signal.

15 . The method of claim 9 , wherein the outgoing video stream further comprises an audio signal, and wherein the one or more optimization actions comprise applying one or more filters to the audio signal of the incoming video stream to reduce a range of audio frequencies.

16 . A communication system, comprising:

one or more processors configured to:

receive, at a server, an outgoing video stream from a transmitting device to a receiving device;

identify a first set of video parameter data corresponding to a quality of the outgoing video stream, wherein the one or more processors are configured to:

train a machine learning (ML) model to determine a set of video parameters related to video stream optimization;

input the outgoing video stream into the trained ML model; and

identify, via the trained ML model, video parameter data from the outgoing video stream correlating to each of the determined video parameters;

receive, from the receiving device, a second set of video parameter data corresponding to the quality of the video stream;

determine one or more quality discrepancies between the first set of video parameter data and the second set of video parameter data; and

provide notification of at least a subset of the quality discrepancies to one or both of the transmitting device and the receiving device.

17 . The communication system of claim 16 , wherein the one or more processors are further configured to:

receive, at the server, a plurality of additional outgoing video streams from a plurality of additional transmitting devices to be transmitted to the receiving device as a plurality of incoming video streams;

identify additional sets of video parameter data corresponding to the quality of the additional outgoing video streams;

determine one or more additional quality discrepancies between the additional sets of video parameter data and the second set of video parameter data; and

provide notification of at least a subset of the additional quality discrepancies to one or more of: the corresponding transmitting device, and the receiving device.

18 . The communication system of claim 17 , wherein a plurality of different shared content is being presented within a plurality of the additional outgoing video streams, and the one or more processors are further configured to:

determine that a subset of the different shared content is not being displayed at the receiving device; and

perform one or more quality reductions to one or more video parameters for the subset of the different shared content that is not being displayed at the receiving device.

19 . A non-transitory computer-readable medium containing instructions, that when executed by one or more processors, causes the one or more processors to perform operations, comprising:

receiving, at a server, an outgoing video stream from a transmitting device to a receiving device;

identifying a first set of video parameter data corresponding to a quality of the outgoing video stream, wherein identifying the first set of video parameter data comprises:

training a machine learning (ML) model to determine a set of video parameters related to video stream optimization;

inputting the outgoing video stream into the trained ML model; and

identifying, via the trained ML model, video parameter data from the outgoing video stream correlating to each of the determined video parameters;

receiving, from the receiving device, a second set of video parameter data corresponding to the quality of the video stream;

determining one or more quality discrepancies between the first set of video parameter data and the second set of video parameter data; and

providing notification of at least a subset of the quality discrepancies to one or both of the transmitting device and the receiving device.

20 . The non-transitory computer-readable medium of claim 19 , wherein determining the one or more quality discrepancies between the first set of video parameter data and the second set of video parameter data is performed using one or more ML techniques.